Methods for Automatic Groove Identificatoin in 3D Bullet Land Scans

Background: Bullet Lands

• When a gun is fired, the bullet is propelled forward through the barrel
  
• As it travels down the barrel, it makes contact with parts of the barrel
  
• Striations result from this contact
  
• Striations are observed on lands. Lands are separated by grooves.

Background: Collection of Land Data

• Sensofar Confocal Light Microscope
  
• Hamby set 44 - 35 bullets from 10 consecutively rifled Ruger barrels
  • Rescanned with CSAFE’s microscope
    
  • To use as ‘base’ set; methods to be tested on several other data sets
  • Have manual groove identifications for this whole set
    
  • Each pixel: .645 square microns
  • Each land is 2mm (2000 microns) wide
• A scan of one bullet (6 individual lands) takes ~1 hour

Background: Land Surface

Importance of Groove Removal

• Bullet matching algorithm
  
• Removing the underlying curved structure of land
• Looking at remaining residuals
  • Deviations from the natural curve of the land

Importance of Groove Removal

• Error Rates
  • Traditional rifling and manual identification of grooves
    • Bullet-to-bullet matching error rate is 0.
  • Polygonal rifling and coated bullets
    • Error rate is higher
      
  • Biggest error source: misidentification of groove locations

Shortcomings of Current Methods

Robust Linear Models

Groove Identification Process

• Once we have the residuals, how do we decide where the cutoffs should be?
  

Comparing two automated approaches

• We will look at all the residuals in the areas between our predicted grooves and the manually identified grooves
  
• For each land in the data set (208 total), sum up these residuals

Comparing two automated approaches

• The sums shown previously are calculated for each land and each method
• Then, we compare the distributions of those values

Impact for Forensic Analysts

• Currently: Individual case studies
  
• Collaborations with different police depts. and forensic institutes
  • Phoenix PD, Los Angeles PD, St. Louis PD, Denver PD
    
  • Story County Sheriff’s Office (IA)
  • Houston Forensic Science Center

Impact for Forensic Analysts

• Future goals:
  • Get algorithm certified
    • no certification process in place for admissibility
      
  • Adhere to OSAC’s updated standards for 3D measurements (Feb. 9, 2018)
    
  • Support firearms examiners in their job
  • Address concerns raised by NRC 2009 and PCAST 2016
    • removing subjectivity from the assessment
    • allow for quantification of error rates

Acknowledgments

• All work was sponsored by CSAFE (Center for Statistics and Applications in Forensic Evidence), a NIST Center of Excellence
  
• Work advised by Drs. Heike Hofmann and Ulrike Genschel of CSAFE/Iowa State University